Abstract
Today, the rapid increase in phenotypic and genotypic information is leading to larger mixed model equations (MMEs) and rendering genetic evaluation more time-consuming. It has been demonstrated that a preconditioned conjugate gradient (PCG) algorithm via an iteration on data (IOD) technique is the most efficient method of solving MME at a low computing cost. Commonly used software applications implementing PCG by IOD merely employ functions from the Intel Math Kernel Library (MKL) to accelerate numerical computations and have not taken full advantage of the multicores or multiprocessors of computer systems to reduce the execution time. Making the most of multicore/multiprocessor systems, we propose PIBLUP, a parallel, shared memory implementation of PCG by IOD to minimize the execution time of genetic evaluation. In addition to functions in MKL, PIBLUP uses Message Passing Interface (MPI) shared memory programming to parallelize code in the entire workflow where possible. Results from the analysis of the two datasets show that the execution time was reduced by more than 80% when solving MME using PIBLUP with 16 processes in parallel, compared to a serial program using a single process. PIBLUP is a high-performance tool for users to efficiently perform genetic evaluation. PIBLUP with its user manual is available at https://github.com/huiminkang/PIBLUP.